Gianluigi Pillonetto

Orcid: 0000-0001-9584-3323

According to our database1, Gianluigi Pillonetto authored at least 157 papers between 2001 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Kernel-based linear system identification: When does the representer theorem hold?
Autom., January, 2024

2023
Sliding-Mode Theory Under Feedback Constraints and the Problem of Epidemic Control.
SIAM J. Appl. Math., December, 2023

Kernel-based learning of orthogonal functions.
Neurocomputing, 2023

Kernel-based function learning in dynamic and non stationary environments.
CoRR, 2023

On the stability test for reproducing kernel Hilbert spaces.
CoRR, 2023

Absolute integrability of Mercer kernels is only sufficient for RKHS stability.
CoRR, 2023

Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression.
CoRR, 2023

Deep networks for system identification: a Survey.
CoRR, 2023

2022
Model-Free Radio Map Estimation in Massive MIMO Systems via Semi-Parametric Gaussian Regression.
IEEE Wirel. Commun. Lett., 2022

Linear Model Identification for Personalized Prediction and Control in Diabetes.
IEEE Trans. Biomed. Eng., 2022

Sample Complexity and Minimax Properties of Exponentially Stable Regularized Estimators.
IEEE Trans. Autom. Control., 2022

Deep prediction networks.
Neurocomputing, 2022

Sparse estimation in linear dynamic networks using the stable spline horseshoe prior.
Autom., 2022

Linear system identification using the sequential stabilizing spline algorithm.
Autom., 2022

Closed-form expressions and nonparametric estimation of COVID-19 infection rate.
Autom., 2022

Bayesian frequentist bounds for machine learning and system identification.
Autom., 2022

Dealing with collinearity in large-scale linear system identification using Bayesian regularization.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Finite-Sample Guarantees for State-Space System Identification Under Full State Measurements.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
Estimation of sparse linear dynamic networks using the stable spline horseshoe prior.
CoRR, 2021

Stable and robust LQR design via scenario approach.
Autom., 2021

Kernel-based methods for Volterra series identification.
Autom., 2021

COVID-19 epidemic control using short-term lockdowns for collective gain.
Annu. Rev. Control., 2021

2020
Kernel Absolute Summability Is Sufficient but Not Necessary for RKHS Stability.
SIAM J. Control. Optim., 2020

Tracking the time course of reproduction number and lockdown's effect during SARS-CoV-2 epidemic: nonparametric estimation.
CoRR, 2020

Efficient spatio-temporal Gaussian regression via Kalman filtering.
Autom., 2020

On the mathematical foundations of stable RKHSs.
Autom., 2020

A convex approach to robust LQR.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
Distributed Multi-Agent Gaussian Regression via Finite-Dimensional Approximations.
IEEE Trans. Pattern Anal. Mach. Intell., 2019

Boosting as a kernel-based method.
Mach. Learn., 2019

Kernel absolute summability is only sufficient for RKHS stability.
CoRR, 2019

A novel Multiplicative Polynomial Kernel for Volterra series identification.
CoRR, 2019

Stable spline identification of linear systems under missing data.
Autom., 2019

Fast robust methods for singular state-space models.
Autom., 2019

System Identification: A Machine Learning Perspective.
Annu. Rev. Control. Robotics Auton. Syst., 2019

Robust Singular Smoothers for Tracking Using Low-Fidelity Data.
Proceedings of the Robotics: Science and Systems XV, 2019

Bayesian Kernel-Based Linear Control Design.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Proprioceptive Robot Collision Detection through Gaussian Process Regression.
Proceedings of the 2019 American Control Conference, 2019

2018
Generalized System Identification with Stable Spline Kernels.
SIAM J. Sci. Comput., 2018

System identification using kernel-based regularization: New insights on stability and consistency issues.
Autom., 2018

The quest for the right kernel in Bayesian impulse response identification: The use of OBFs.
Autom., 2018

Prediction-error identification of LPV systems: A nonparametric Gaussian regression approach.
Autom., 2018

On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses.
Autom., 2018

The generalized cross validation filter.
Autom., 2018

Uncertainty Bounds for Kernel-based Regression: a Bayesian SPS Approach.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures.
Proceedings of the 16th European Control Conference, 2018

Non-Invasive Continuous-Time Blood Pressure Estimation from a Single Channel PPG Signal using Regularized ARX Models.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

A New Model Selection Approach to Hybrid Kernel-Based Estimation.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017
Distributed multi-agent Gaussian regression via Karhunen-Loève expansions.
CoRR, 2017

Multi-robots Gaussian estimation and coverage control: From client-server to peer-to-peer architectures.
Autom., 2017

Maximum Entropy vector kernels for MIMO system identification.
Autom., 2017

A new kernel-based approach to system identification with quantized output data.
Autom., 2017

Generalized Kalman smoothing: Modeling and algorithms.
Autom., 2017

Statistical bounds for Gaussian regression algorithms based on Karhunen-Loève expansions.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016
Newton-Raphson Consensus for Distributed Convex Optimization.
IEEE Trans. Autom. Control., 2016

The interplay between system identification and machine learning.
CoRR, 2016

Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint.
Autom., 2016

A new kernel-based approach to hybrid system identification.
Autom., 2016

Maximum entropy properties of discrete-time first-order stable spline kernel.
Autom., 2016

Robust EM kernel-based methods for linear system identification.
Autom., 2016

A stable spline convex approach to hybrid systems identification.
Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

On-line Bayesian system identification.
Proceedings of the 15th European Control Conference, 2016

Classical vs. Bayesian methods for linear system identification: Point estimators and confidence sets.
Proceedings of the 15th European Control Conference, 2016

Continuous-time DC kernel - A stable generalized first order spline kernel.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

Machine learning meets Kalman Filtering.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016

2015
The Connection Between Bayesian Estimation of a Gaussian Random Field and RKHS.
IEEE Trans. Neural Networks Learn. Syst., 2015

Bayesian kernel-based system identification with quantized output data.
CoRR, 2015

Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator.
Autom., 2015

Identification of hybrid systems using stable spline kernels.
Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

Auto-tuning procedures for distributed nonparametric regression algorithms.
Proceedings of the 14th European Control Conference, 2015

Identification of stable models via nonparametric prediction error methods.
Proceedings of the 14th European Control Conference, 2015

Multi-agents adaptive estimation and coverage control using Gaussian regression.
Proceedings of the 14th European Control Conference, 2015

Perspectives of orthonormal basis functions based kernels in Bayesian system identification?
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Bayesian identification of LPV Box-Jenkins models.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Spectral analysis of the DC kernel for regularized system identification.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Outlier robust kernel-based system identification using ℓ1-Laplace techniques.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

2014
Distributed Cardinality Estimation in Anonymous Networks.
IEEE Trans. Autom. Control., 2014

System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques.
IEEE Trans. Autom. Control., 2014

Robust and Trend-Following Student's t Kalman Smoothers.
SIAM J. Control. Optim., 2014

Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso.
J. Mach. Learn. Res., 2014

Kernel methods in system identification, machine learning and function estimation: A survey.
Autom., 2014

A new kernel-based approach for identification of time-varying linear systems.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Kalman smoothing with persistent nuisance parameters.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Tuning complexity in kernel-based linear system identification: The robustness of the marginal likelihood estimator.
Proceedings of the 13th European Control Conference, 2014

Bayesian and nonparametric methods for system identification and model selection.
Proceedings of the 13th European Control Conference, 2014

Bayesian and regularization approaches to multivariable linear system identification: The role of rank penalties.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

On the design of multiple kernels for nonparametric linear system identification.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
Finding Potential Support Vectors in Separable Classification Problems.
IEEE Trans. Neural Networks Learn. Syst., 2013

Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory.
J. Mach. Learn. Res., 2013

The exact relationship between regularization in RKHS and Bayesian estimation of Gaussian random fields
CoRR, 2013

A multi-task learning approach for the extraction of single-trial evoked potentials.
Comput. Methods Programs Biomed., 2013

Distributed Kalman smoothing in static Bayesian networks.
Autom., 2013

Consistent identification of Wiener systems: A machine learning viewpoint.
Autom., 2013

Regularized spectrum estimation using stable spline kernels.
Autom., 2013

Kernel-based model order selection for identification and prediction of linear dynamic systems.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Regularization strategies for nonparametric system identification.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Rank-1 kernels for regularized system identification.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Linear system identification using stable spline kernels and PLQ penalties.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

Kernel-Based Model Order Selection for Linear System Identification.
Proceedings of the 11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013

2012
Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework.
IEEE Trans. Wirel. Commun., 2012

Distributed parametric and nonparametric regression with on-line performance bounds computation.
Autom., 2012

A Bayesian approach to sparse dynamic network identification.
Autom., 2012

Online Estimation of Covariance Parameters using Extended Kalman Filtering and Application to Robot Localization.
Adv. Robotics, 2012

The convergence rate of Newton-Raphson consensus optimization for quadratic cost functions.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Bayesian learning of probability density functions: A Markov chain Monte Carlo approach.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Sparse multiple kernels for impulse response estimation with majorization minimization algorithms.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

On the estimation of hyperparameters for Bayesian system identification with exponentially decaying kernels.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Nonsmooth regression and state estimation using piecewise quadratic log-concave densities.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Multidimensional Newton-Raphson consensus for distributed convex optimization.
Proceedings of the American Control Conference, 2012

Consensus based estimation of anonymous networks size using Bernoulli trials.
Proceedings of the American Control Conference, 2012

Regularized spectrum estimation in spaces induced by stable spline kernels.
Proceedings of the American Control Conference, 2012

2011
Client-Server Multitask Learning From Distributed Datasets.
IEEE Trans. Neural Networks, 2011

A New Kernel-Based Approach for NonlinearSystem Identification.
IEEE Trans. Autom. Control., 2011

An <sub>1</sub> -Laplace Robust Kalman Smoother.
IEEE Trans. Autom. Control., 2011

Prediction error identification of linear systems: A nonparametric Gaussian regression approach.
Autom., 2011

Learning Output Kernels with Block Coordinate Descent.
Proceedings of the 28th International Conference on Machine Learning, 2011

Kernel selection in linear system identification Part I: A Gaussian process perspective.
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

On the discardability of data in support vector classification problems.
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

Convex vs nonconvex approaches for sparse estimation: Lasso, Multiple Kernel Learning and Hyperparameter Lasso.
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

2010
Bayesian Online Multitask Learning of Gaussian Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

A new kernel-based approach for linear system identification.
Autom., 2010

Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

The unconstrained and inequality constrained moving horizon approach to robot localization.
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

Distributed statistical estimation of the number of nodes in sensor networks.
Proceedings of the 49th IEEE Conference on Decision and Control, 2010

Nonparametric sparse estimators for identification of large scale linear systems.
Proceedings of the 49th IEEE Conference on Decision and Control, 2010

Distributed consensus-based Bayesian estimation: sufficient conditions for performance characterization.
Proceedings of the American Control Conference, 2010

Regularized estimation of sums of exponentials in spaces generated by stable spline kernels.
Proceedings of the American Control Conference, 2010

2009
Nonlinear Stochastic Regularization to Characterize Tissue Residue Function in Bolus-Tracking MRI: Assessment and Comparison With SVD, Block-Circulant SVD, and Tikhonov.
IEEE Trans. Biomed. Eng., 2009

Fast algorithms for nonparametric population modeling of large data sets.
Autom., 2009

Fast computation of smoothing splines subject to equality constraints.
Autom., 2009

An inequality constrained nonlinear Kalman-Bucy smoother by interior point likelihood maximization.
Autom., 2009

Distributed function and time delay estimation using nonparametric techniques.
Proceedings of the 48th IEEE Conference on Decision and Control, 2009

A Bayesian learning approach to linear system identification with missing data.
Proceedings of the 48th IEEE Conference on Decision and Control, 2009

2008
Identification of Time-Varying Systems in Reproducing Kernel Hilbert Spaces.
IEEE Trans. Autom. Control., 2008

Client-server multi-task learning from distributed datasets
CoRR, 2008

Optimal smoothing of non-linear dynamic systems via Monte Carlo Markov chains.
Autom., 2008

Solutions of nonlinear control and estimation problems in reproducing kernel Hilbert spaces: Existence and numerical determination.
Autom., 2008

Wavelet estimation by Bayesian thresholding and model selection.
Autom., 2008

Online estimation of variance parameters: Experimental results with applications to localization.
Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

Predictor estimation via Gaussian regression.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

Subspace identification using predictor estimation via Gaussian regression.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

Bayesian online multi-task learning using regularization networks.
Proceedings of the American Control Conference, 2008

Predictive power of indices derived from models of biological dynamic systems.
Proceedings of the American Control Conference, 2008

A new kernel-based approach for system identification.
Proceedings of the American Control Conference, 2008

2007
Identifiability of the stochastic semi-blind deconvolution problem for a class of time-invariant linear systems.
Autom., 2007

Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance.
Autom., 2007

Multirobot localization with unknown variance parameters using iterated Kalman filtering.
Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29, 2007

Efficient Nonparametric Population Modeling for Large Data Sets.
Proceedings of the American Control Conference, 2007

2006
A new dynamic index of insulin sensitivity.
IEEE Trans. Biomed. Eng., 2006

Input estimation in nonlinear dynamical systems using differential algebra techniques.
Autom., 2006

Glucose Production by Deconvolution in Intravenous and Oral Glucose Tolerance Tests: Role of Output Variable.
Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

2005
Motion planning using adaptive random walks.
IEEE Trans. Robotics, 2005

Merging the adaptive random walks planner with the randomized potential field planner.
Proceedings of the Fifth International Workshop on Robot Motion and Control, 2005

2003
Bayesian Deconvolution of Functions in RKHS Using MCMC Techniques.
Proceedings of the System Modeling and Optimization, 2003

Robot motion planning using adaptive random walks.
Proceedings of the 2003 IEEE International Conference on Robotics and Automation, 2003

2002
WINSTODEC: a stochastic deconvolution interactive program for physiological and pharmacokinetic systems.
Comput. Methods Programs Biomed., 2002

2001
Reconstructing insulin secretion rate after a glucose stimulus by an improved stochastic deconvolution method.
IEEE Trans. Biomed. Eng., 2001


  Loading...